Reserve the first level headings (#) for the start of a new Module. This will help to organize your portfolio in an intuitive fashion.
Note: Please edit this template to your heart’s content. This is meant to be the armature upon which you build your individual portfolio. You do not need to keep this instructive text in your final portfolio, although you do need to keep module and assignment names so we can identify what is what.
The first of your second level headers (##) is to be used for the portfolio content checks. The Module 01 portfolio check has been built for you directly into this template, but will also be available as a stand-alone markdown document available on the MICB425 GitHub so that you know what is required in each module section in your portfolio. The completion status and comments will be filled in by the instructors during portfolio checks when your current portfolios are pulled from GitHub.
The remaining second level headers (##) are for separating data science Friday, regular course, and project content. In this module, you will only need to include data science Friday and regular course content; projects will come later in the course.
Third level headers (###) should be used for links to assignments, evidence worksheets, problem sets, and readings, as seen here.
Use this space to include your installation screenshots.
Detail the code you used to create, initialize, and push your portfolio repo to GitHub. This will be helpful as you will need to repeat many of these steps to update your porfolio throughout the course.
Paste your code from the in-class activity of recreating the example html.
version January 18, 2018
The following assignment is an exercise for the reproduction of this .html document using the RStudio and RMarkdown tools we’ve shown you in class. Hopefully by the end of this, you won’t feel at all the way this poor PhD student does. We’re here to help, and when it comes to R, the internet is a really valuable resource. This open-source program has all kinds of tutorials online.
http://phdcomics.com/ Comic posted 1-17-2018
The goal of this R Markdown html challenge is to give you an opportunity to play with a bunch of different RMarkdown formatting. Consider it a chance to flex your RMarkdown muscles. Your goal is to write your own RMarkdown that rebuilds this html document as close to the original as possible. So, yes, this means you get to copy my irreverant tone exactly in your own Markdowns. It’s a little window into my psyche. Enjoy =)
hint: go to the PhD Comics website to see if you can find the image above If you can’t find that exact image, just find a comparable image from the PhD Comics website and include it in your markdown
Let’s be honest, this header is a little arbitrary. But show me that you can reproduce headers with different levels please. This is a level 3 header, for your reference (you can most easily tell this from the table of contents).
Perhaps you’re already really confused by the whole markdown thing. Maybe you’re so confused that you’ve forgotton how to add. Never fear! A calculator R is here:
1231521+12341556280987
## [1] 1.234156e+13
Or maybe, after you’ve added those numbers, you feel like it’s about time for a table! I’m going to leave all the guts of the coding here so you can see how libraries (R packages) are loaded into R (more on that later). It’s not terribly pretty, but it hints at how R works and how you will use it in the future. The summary function used below is a nice data exploration function that you may use in the future.
library(knitr)
kable(summary(cars),caption="I made this table with kable in the knitr package library")
| speed | dist | |
|---|---|---|
| Min. : 4.0 | Min. : 2.00 | |
| 1st Qu.:12.0 | 1st Qu.: 26.00 | |
| Median :15.0 | Median : 36.00 | |
| Mean :15.4 | Mean : 42.98 | |
| 3rd Qu.:19.0 | 3rd Qu.: 56.00 | |
| Max. :25.0 | Max. :120.00 |
And now you’ve almost finished your first RMarkdown! Feeling excited? We are! In fact, we’re so excited that maybe we need a big finale eh? Here’s ours! Include a fun gif of your choice!
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The template for the first Evidence Worksheet has been included here. The first thing for any assignment should link(s) to any relevant literature (which should be included as full citations in a module references section below).
You can copy-paste in the answers you recorded when working through the evidence worksheet into this portfolio template.
As you include Evidence worksheets and Problem sets in the future, ensure that you delineate Questions/Learning Objectives/etc. by using headers that are 4th level and greater. This will still create header markings when you render (knit) the document, but will exclude these levels from the Table of Contents. That’s a good thing. You don’t’ want to clutter the Table of Contents too much.
Describe the numerical abundance of microbial life in relation to ecology and biogeochemistry of Earth systems.
How do we estimate the prokaryotic population of the world? And what is it made up of?
What are the uncertainties that come with this measurement?
Which environments contain the most prokaryotic biomass?
How does this biomass affect global nutrient cycles? (e.g. P, C, N)
Prokaryotic estimates were based upon average data from the following four environments: aquatic environments, soil, subsurface, and “other habitats” including in or on animal or plant surfaces or in the air. They used experimentally derived values to perform these calculations, but interestingly, not the same value sets for each environment. For example, some calculations included cell volume, while others included just the area of the environment. Vi
Also, they compared their calculated values with some from other papers, which resulted in some differences that they attempted to explain.
They found that prokaryotes contain about half of the organic carbon on earth, and 90% of the nutrients (compared to plants) In brief, the prokaryotic biomass and thus their contribution to global cycles is very large - doubling estimates of the amount of carbon stored in living organisms globally. They broke down the calculations into four environments: aquatic environments, soil, subsurface, and “other habitats”.
Aquatic environments- this includes the open ocean, sediment in the ocean, freshwater and saline lakes (3 orders of magnitude less) and polar regions. Prokaryotes are ubiquitous in these environments - 1180 x 1026 cells.
Soil- surprisingly, there are less prokaryotes in forest soils than in other soils. The estimates varied by ecosystem. 255.6 x 1027 cells.
Subsurface- e.g. terrestrial habitats below 8 m and marine sediments below 10 cm. (this includes groundwater too) This environment is difficult to estimate because it is difficult to obtain uncontaminated samples. However, it has been suggested to be enormous. 3.8 x 1030 cells.
Other environments - discussed the prokaryotes that live on animals, insects, and plants, and also those in the air/atmosphere. 53.024 x 1023 cells (several orders of magnitude smaller)
These large numbers mean that not only carbon, but N and P are stored in globally significant amounts in prokaryotes.
Disproves Kluyver’s estimate that 1/2 of the living protoplasm on earth is microbial - likely this number is far too conservative. The paper also discusses growth rates to estimate cell turnover, and fluxes in and out of these environments.
In subsurface environments, the turnover time of cells seems exceedingly large, is this a good estimate?
Where does the energy in the subsurface environments come from? Photosynthesis? Chemolithotrophy?
From the passage “in the polar regions, a relatively dense community of algae and prokaryotes forms at the water-ice interface” - why does this occur?
How accurate can these calculations be if they are based upon just a few estimates?
How much flux occurs among all of these prokaryotic environments? Especially the subsurface environment, if so many cells are hypothesized to be metabolically inactive, how much flux can occur? Is it more of a pool than a flux?
The estimates described in the paper introduce a large amount of uncertainty because no matter how many samples you collect, you are still having to generalize this data for the entire earth. You cannot possibly collect enough data to have any degree of accuracy in your prediction. However, otherwise, their experimental logic made sense.
Lastly, the estimates for each environment were likely collected using different methods- and by different people. Therefore, each method probably has its own pros and cons, and contributes its own level of uncertainty to the proceeding estimates.
Describe the numerical abundance of microbial life in relation to the ecology and biogeochemistry of Earth systems.
The primary prokaryotic habitats on earth are split into aquatic habitats, soil, and subsurface habitats. According to table 5 of the text, there are 12 x 1028 cells in aquataic habitats, 26 x 1028 cells in the soil, and interestingly, 355 x 1028 and 25-250 x 1028 prokaryotic cells in the oceanic and terrestrial subsurface respectively. However, in order to rank these habitats based on their capacity to support life, we must come up with a universal definition for “capacity to support life”. If you were to define this as the total number of prokaryotic cells in a given habitat, it would appear that the oceanic subsurface habitat has the greatest capacity to support life. However, this does not take into account whether these cells are metabolically active, or their turnover time, or the total area occupied by the habitat.
2.8 x 1028 cells in the upper 200m
The average density is 5 x 105 cells/mL
To calculate what fraction of this ratio are cyanobacteria:
4 x104 cells/ml / 5 x 105 cells/ml x 100 = 8%
This ratio is significant because these cells are autotrophs, which means that they are responsible for asimilating inorganic carbon into this environment, and thus are an important aspect of carbon cycling in the ocean. This is not only important for aquatic environments, but for the atmospheric composition of the earth as well. This is because some organic carbon fixed by these autotrophs are not respired and stored in marine sediment. Since respiring this material generally requires oxygen, its long-tem storage means that oxygen can remain in significant levels in the earth’s atmosphere. This is in contrast to terrestrial systems, where carbon fixed by autotrophs is generally respired.
Autotroph - produces organic complex carbons from simple inorganic substances such as carbon dioxide. Heterotroph - takes up organic carbon to produce energy and synthesize compounds Lithotroph - uses an inorganic substrate to obtain reducing equivalents for use in biosynthesis or energy conservation via aerobic or anaerobic respiration
Temperature is the limiting factor in subsurface environments, and at around 4 km in terestrial environments the temperature reaches 125 degrees celsius. This is the generally agreed upon temperature limit of prokaryotic life.
The deepest habitat capable of supporting prokaryotic life is the Mariana Trench, which is 10.9 km deep, then cellular life should be able to persist another 4 km deeper in the subsurface- so 14.9 km total.
Mount everest is 8.8 km high, so that would be the highest terestrial habitat capable of supporting prokaryotic life.
Additionally, in the text, bacteria in the air were discussed. However, are these bacteria actually metabolically active? They could just be spore formers, or metabolically inactive until they reach an environment that is more viable.
The paper stated 77 km, but this does not seem very realistic, because the limiting factors in these environments include nutrient availability, UV radiation, and temperature. So I would say more like 20 km high.
I would say that the vertical distance of the Earth’s biosphere is a range of 24 km - 34 km (due to the Mariana trench)
Population size x turnovers/year = cells/year
Marine heterotrophs: 3.6 x 1028 cells x 365 days / 16 days/turnover = 8.2 x 1029 cells/year
Assuming the carbon efficiency is 20% If there is around 5-20 fg of carbon in a prokaryotic cell, 20 fg C/cell = 20-30 Pg/cell
3.6 x 1028 cells x 20-30 Pg/cell = 0.72 Pg C are trapped in marine heterotrophs To calculate the total carbon flux, we should multiply this value by 5, but the authors used 4, so 4 x .72 = 2.88 Pg/year
51 Pg C/year, 85% of the carbon in the photic zone is consumed = 43 Pg C
43 Pg C/year / 2.88 Pg/year = 14.4 turnovers/year 1 turnover every 25.4 days
This varies with depth in the ocean due to access to sunlight, as photosynthesis provides the energy necessary for carbon fixation. In terestrial habitats, a number of factors including differences in depth, sediment, nutrient availability, and cell density contribute to the differences in carbon fixation and turnover rate.
Given the large population size and high mutation rate of prokaryotic cells, this indicates that prokaryotic cells have a very large adaptive potential. As prokaryotes have existed on the earth for billions of years, this corresponds to an incredibly large amount of genetic diversity.
However, it is foolhardy to assume that point mutations are the only way that microbial genomes diversify and adapt. Infection by bacteriophages can transport foreign DNA into a cell, some cells can share plasmids using conjugative pili, and others can uptake exogenous DNA from their environment. Additionally, other mutation events can occur independent of point mutations in a single cell, such as gene duplication or deletion.
Based on the information provided in the text,
Utilize this space to include a bibliography of any literature you want associated with this module. We recommend keeping this as the final header under each module.
An example for Whitman and Wiebe (1998) has been included below.
Whitman WB, Coleman DC, and Wiebe WJ. 1998. Prokaryotes: The unseen majority. Proc Natl Acad Sci USA. 95(12):6578–6583. PMC33863